class_docstr: Superclass to implement Hamiltonian/hybrid monte carlo.
code: def reset(self, start=None):
      self.tune = True
      self.potential.reset()
  
imports: from __future__ import annotations
  import logging
  import time
  from abc import abstractmethod
  from typing import Any, NamedTuple
  import numpy
  from pymc.blocking import DictToArrayBijection, RaveledVars, StatsType
  from pymc.exceptions import SamplingError
  from pymc.model import Point, modelcontext
  from pymc.pytensorf import floatX
  from pymc.stats.convergence import SamplerWarning, WarningType
  from pymc.step_methods import step_sizes
  from pymc.step_methods.arraystep import GradientSharedStep
  from pymc.step_methods.hmc import integration
  from pymc.step_methods.hmc.integration import IntegrationError, State
  from pymc.step_methods.hmc.quadpotential import QuadPotentialDiagAdapt, quad_potential
  from pymc.tuning import guess_scaling
  from pymc.util import get_value_vars_from_user_vars
label_desc: Use image node_id 5 for calling the BaseHMC obj's underlying member method code with example usage: obj.reset(start) without return types